Within-day travel speed pattern unsupervised classification – A data driven case study of the State of Alabama during the COVID-19 pandemic
| Autor: | Rui Ma, Niloufar Shirani-bidabadi, Michael Anderson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
Dynamic time warping Unsupervised classification TA1001-1280 Coronavirus disease 2019 (COVID-19) Computer science 05 social sciences 0211 other engineering and technologies COVID-19 Transportation 02 engineering and technology Within-day traffic dynamics Hierarchical clustering Data-driven Transportation engineering New normal Distance matrix 021105 building & construction 0502 economics and business Statistics Classification methods State (computer science) Civil and Structural Engineering |
| Zdroj: | Journal of Traffic and Transportation Engineering (English ed. Online), Vol 8, Iss 2, Pp 170-185 (2021) |
| ISSN: | 2095-7564 |
| Popis: | Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders, which however may not accurately present the actual impacted dates The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order, the methodology in this study investigates the within-day time-dependent travel speed as time series, and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group Using the state-wide travel speed data in Alabama, these study measures dissimilarities among within-day travel speed time series By incorporating the dissimilarities/distance matrix, various agglomerative hierarchical clustering (AHC) methods (average, complete, Ward's) are tested to conduct proper unsupervised classification The Ward's AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order The results further show that a new travel speed pattern appears at the end of stay-at-home order, which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern, which leads to a conclusion that a ‘new normal’ within-day travel pattern emerges © 2021 The Authors |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|